Predicting Marketing Campaign Effectiveness through Consumer Personality Analysis using Machine Learning

Understanding customer behavior and personality is essential for marketing campaign. It might help huge corporations comprehend their purchasing behavior. Companies directed by their marketers can conduct product marketing activities that are specific to the customer’s personality attributes. This study examines the use of various classification models to evaluate client personality attributes and predict marketing campaign performance by customer response after marketing campaign. Response of the customer was the target feature and it is a binary classification. Feature engineering has also been applied for optimal feature selection. Several machine learning classifier algorithms, including Gradient Boosting (GB), Ada Boost (AB), Cat Boost (CB), Voting Classifier (VC), Stacking Classifier (SC), K Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) were implemented after the dataset had been preprocessed and normalized. Several matrices were employed for comparing the performance of these models. The Stacking Classifier emerged to be the best performing model with accuracy of 0.8988.